A new article is available in IPOL: https://www.ipol.im/pub/art/2024/549/
Roland Akiki, Jérémy Anger, Carlo de Franchis, Gabriele Facciolo,
Raphaël Grandin, and Jean-Michel Morel,
A Brief Evaluation of InSAR Phase Denoising and Coherence Estimation
with Phi-Net,
Image Processing On Line, 14 (2024), pp. 205–216.
https://doi.org/10.5201/ipol.2024.549
Abstract
In this article, we examine the joint InSAR phase denoising and
coherence estimation performance of the network known as Phi-Net [Sica
et al., IEEE Transactions on Geoscience and Remote Sensing, 2021]. We
briefly examine the method, network architecture, training data and
strategy. Then, in the experimental section, we compare the network's
performance against the simple boxcar uniform filter. We verify the
observations made by the authors, in particular concerning the superior
denoising performance and preservation of fine details in the coherence
estimation. Our experiments also indicate that an end-to-end deep
learning method might bring a small improvement to the patch-based
approach adopted in Phi-Net.
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